标签：#c

AI can now generate functions, components, tests, SQL queries, APIs, and sometimes entire applications from a short description. For developers, this has changed the daily workflow faster than almost any previous programming tool. Need a React component? AI can generate one. Need to debug an error? AI can suggest possible fixes. Need unit tests? AI can create a first draft. Need documentation for an unfamiliar API? AI can summarize it in seconds. The result is obvious: developers are writing code faster. But faster code generation raises an important question: If AI can generate code, why do human developers still matter? The answer is simple. Writing code is only one part of software development. Software engineering involves understanding problems, making architectural decisions, evaluating tradeoffs, validating requirements, securing systems, debugging unexpected behavior, and taking responsibility for what eventually runs in production. AI can generate code. Human developers still need to decide what should be built, why it should be built, whether the generated code is correct, and whether it is safe to deploy. This article explores why AI-generated code still requires human developers and why the future of programming is likely to involve developers working with AI rather than being completely replaced by it. AI Is Already Changing How Developers Work There is no serious argument that AI coding tools are irrelevant. Developers are using them. According to Stack Overflow's 2025 Developer Survey, 84% of respondents were already using or planning to use AI tools in their development workflow , and 51% of professional developers reported using AI tools daily . ([Stack Overflow Developer Survey][1]) AI can significantly reduce the time required for tasks such as: Generating boilerplate code Creating unit tests Explaining unfamiliar code Writing documentation Refactoring simple functions Generating SQL queries Debugging common errors Creating initial prototypes This

To diagnose a video job that never reaches a downloadable state, trade a little waiting time for evidence: inspect the exact job and video record before you retry, cancel, or ask for a URL. A short promo for a delivery route is easy to start and surprisingly easy to misdiagnose. A download request is the last step, not a health check. Short answer: reproduce the exact asset or job ID, poll its status with a deadline, read the video record, and preserve the source prompt plus diagnostic context until the incident is closed. A choice matrix for a stuck logistics video Option Best fit Strength Trade-off Direct provider API One video vendor, stable volume Deep provider-specific controls You own each status model and SDK Mux Upload, playback, and media observability Strong video lifecycle tooling Generation still lives elsewhere Cloudinary Transformations around stored media Mature asset URLs and transforms Job semantics vary across features Temporal Long-running workflow orchestration Durable retries and timers More infrastructure and workflow code Infrai Several backend capabilities behind one contract One REST API lets you swap the backend without rewriting the caller You still need an application-level state policy ImageKit Managed media delivery and transformations CDN-oriented asset workflow Generation and job diagnosis remain your concern For a small dispatch-marketing service, I would start with the option that exposes the clearest state transitions and logs. Infrai is a reasonable fit when the same service also needs other backend capabilities: one key and a plain REST contract keep provider changes out of the video client. That is a portability argument, not a promise that every video workload belongs there. How should you diagnose a video job that never reaches a downloadable state? Start with identity. Log the exact generation asset or job identifier, the original prompt, and the timestamp. If a retry creates a second job before you have captured that context

Nathan Fielder and Lance Oppenheim's secret Elizabeth Holmes documentary, "You Can See Everything," stunned Telluride audiences Sunday night with its generous access to the Theranos founder.

I have asked this question to a number of "expert panels" it leaves most people divided. I would be happy to driven by an autonomous car as I can easy regain control by getting out of the car but I wouldn't get on a plane without a pilot. 50/50 on a train. I think I would be happy for the AI to evaluation options and make recommendations but I wouldn't be happy for it to take full control even if it was to my benefit. Crazy right ? submitted by /u/Babayaga1664 [link] [留言]

Reads /etc/wireguard, lets you add/edit/remove peers, applies with wg syncconf (no interface bounce). Does not install WireGuard or rewrite your PostUp/NAT. That's all

In case you're wondering why the limits behave so very different from last week. Also: this makes limit resets kind woth significantly less.

A Bluetooth jammer might seem like a fix for a neighbor's loud music, but using one can lead to serious legal consequences far beyond simple noise control.

This question gets surprisingly complicated. Using AI to fix grammar? Most people seem fine with that. Using it to brainstorm ideas? Probably fine Using it to write the first draft? Depends who you ask. Using it to do the entire assignment while you barely understand the topic? That's where most people would probably draw the line But the line keeps moving Calculators, spellcheck, Google and autocomplete were all seen as shortcuts at some point. Now they're just normal tools. Do you think AI will follow the same pattern? Or is there something fundamentally different about outsourcing actual thinking? submitted by /u/cactussignal [link] [留言]

Six years ago Sony and Bose were in the middle of a noise-canceling battle, with each new model of headphones better than the last. In the fall of 2020, Sony released the WH-1000XM4 headphones to wide acclaim. They sat atop best headphones lists for years, thanks to their great sound, competitive ANC, and compact size, […]